Binary entropy function

Entropy of a Bernoulli trial as a function of success probability, called the binary entropy function.

In information theory, the binary entropy function, denoted or , is defined as the entropy of a Bernoulli process with probability of success p. Mathematically, the Bernoulli trial is modelled as a random variableX that can take on only two values: 0 and 1. The event is considered a success and the event is considered a failure. (These two events are mutually exclusive and exhaustive.)

where is taken to be 0. The logarithms in this formula are usually taken (as shown in the graph) to the base 2. See binary logarithm.

When the binary entropy function attains its maximum value. This is the case of the unbiased bit, the most common unit of information entropy.

is distinguished from the entropy function in that the former takes a single real number as a parameter whereas the latter takes a distribution or random variables as a parameter. Sometimes the binary entropy function is also written as . However, it is different from and should not be confused with the Rényi entropy, which is denoted as .

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In terms of information theory, entropy is considered to be a measure of the uncertainty in a message. To put it intuitively, suppose . At this probability, the event is certain never to occur, and so there is no uncertainty at all, leading to an entropy of 0. If , the result is again certain, so the entropy is 0 here as well. When , the uncertainty is at a maximum; if one were to place a fair bet on the outcome in this case, there is no advantage to be gained with prior knowledge of the probabilities. In this case, the entropy is maximum at a value of 1 bit. Intermediate values fall between these cases; for instance, if , there is still a measure of uncertainty on the outcome, but one can still predict the outcome correctly more often than not, so the uncertainty measure, or entropy, is less than 1 full bit.